China Summary Translation: 'Hype Cycle for ICT in China, 2019

China Summary Translation: 'Hype Cycle forICT in China, 2019'Published: 9 December 2019 ID: G00464176

Analyst(s): Kevin Ji

This Hype Cycle research analyzes the trends in information andcommunication technology in China, focusing on the impact, adoptionpattern and maturity of 20 technologies.

中国语境由于中美贸易摩擦等诸多原因，未来世界整体经济前景充满不确定，导致中国企业 2019 年 IT 投入趋于理性，很多全球化的推进也主要关注在一带一路沿线的国家和地区。Gartner 每年定期发布针对中国市场的技术成熟度曲线，分析在市场高度关注以及技术不断发展的背景下，各热门技术来到了什么位置，为企业 IT 管理者何时以及如何投资新技术提出建议，它可作为一种重要的技术评估工具。

在 2019 年的中国信息与通信技术（ICT）技术成熟度曲线中，Gartner 选择了 20 种目前市场关注度比较高的技术，特别是增加了量子计算、云分析工具和广域软件定义网络（SD-WAN）。因为国家政策扶持，区块链技术未来在中国也会有很大的发展前景，Gartner 会有专门行业报告对其进行分析。而由于中美贸易摩擦，中美两国在技术使用与供应商选择方面的差异也会进一步加大。

Gartner 认为，2019 年人工智能（AI）、机器人流程自动化（RPA）、区块链与 5G 是 ICT 市场最为关注的技术。中国企业的首席信息官（CIO）可以关注报告中各项技术的成熟度并参照行业的适用度，选择合适的技术，以便在风险可控的前提下，提升 IT 能力，支持数字化转型需求。

本文介绍了中国 ICT 技术发展成熟度曲线，分析了目前各热门新兴技术在中国所处的位置，以及市场普及预计所需要的时间，为中国企业 IT 领导者规划投资开发和新技术采用提供参考。

报告摘录

分析

技术成熟度曲线2019 年 Gartner 中国 ICT 成熟度曲线旨在帮助 CIO 和技术提供商评估中国市场的新兴趋势和技术。为了维持可持续的经济增长，ICT 行业需要构建能力来支持这一趋势，特别是在满足终端用户需求和创造实际业务价值方面。

Gartner 发现，中国 ICT 市场中的大型企业乐于拥抱新的机会，愿意采用开源软件来推动 IT 能力转型。他们已经开始构建新的采购流程，利用初创技术公司来推动业务创新。

在基础设施领域，部署混合云而非单纯的私有云已经成为趋势。考虑到公有云在规模经济和业务工作负载创新方面的价值，Gartner 坚信越来越多的企业客户将会认识到公有云的重要性，以及它对于未来的必要性。Gartner 还注意到，人才短缺和安全与合规的顾虑是企业 IT 部门使用公有云基础设施即服务（IaaS）的最大挑战。

在应用领域，如何实现应用交付模式转型和提高敏捷性已成为最热门的话题，所涉领域包括持续集成（CI）/持续交付（CD）、微服务和容器。Gartner 将应用和基础设施相结合，利用 DevOps 来实现敏捷交付。根据 Gartner 的观察，交付模式转型的主要挑战是组织就绪度。另外，很多客户纷纷利用平台架构来重构应用，推动业务转型。但是，Gartner 观察到的这一方面成功用例目前并不多。

今年的技术成熟度曲线增加了四项新技术，它们在中国刚刚兴起或正受到热炒，处于技术萌芽期或期望膨胀期。这些技术包括 RPA 软件、SD-WAN 托管服务、云分析与商业智能（ABI）、量子计算，它们将对中国 ICT 市场产生越来越大的影响。此外，本报告还将超融合集成系统更名为“超融合”。

同时，本报告移除了四种技术，这些技术或者在中国的发展速度和成熟度与全球发展一致，或者被纳入了中国智慧城市和可持续发展技术成熟度曲线中。

以下是该文件的英文版全文

Hype Cycle for ICT in China, 2019

K. Ji, R. Sheng

This Hype Cycle research analyzes the trends in information and communication technology inChina, focusing on the impact, adoption pattern and maturity of 20 technologies.

Analysis

What You Need to Know

Large enterprises still insisted on investing in new technology to support digital businesstransformation despite U.S.-China trade tension. Compared with last year, the investment is morerational in 2019 and the go-abroad strategy is not as hot. Driven by these trends, the Chineseinformation and communication technology (ICT) industry is focused on building business withdomestic, sustainable and innovative capabilities, especially in artificial intelligence (AI), 5G, dataanalytics, the cloud, robotics and semiconductors.

In the Chinese domestic market, enterprises are focused on new technology that drives operationalefficiency such as software-defined infrastructure and automation area. Besides AI and machinelearning, which are still hot areas, we introduce managed SD-WAN services and robotic process

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automation software as new technologies in this Hype Cycle. In addition, we have added quantumcomputing.

In considering the China context, we selected 20 technologies. Enterprises can leverage this HypeCycle to better understand the Hype Cycle position, user advice and possible business impact ofthe technologies they’re interested in, which will help them decide where and when to invest.Technology vendors can also benefit from a better understanding of what public expectations are inthe hot topic markets and the requirements of these new technologies to refine their go-to-marketstrategies.

The Hype Cycle

The Gartner Hype Cycle for ICT in China, 2019 is designed to help CIOs and technology providersevaluate emerging trends and technologies in the China market. In order to keep sustainableeconomic growth, the ICT industry needs to build its ability to support this trend, especially inaddressing end-user demands and bringing real business value.

In the Chinese ICT market, we have observed that large enterprises welcome the opportunity toadopt open-source software to drive IT capability transformation. They’ve started to build newprocurement processes to embrace startup tech companies for business innovation areas.

For the infrastructure area, rather than pure private cloud adoption, hybrid cloud is the trend ofadoption. Given the value of public cloud in generating economies of scale and business workloadinnovation, we believe more and more enterprise clients will realize that the public cloud is veryimportant and is inevitable in the future. We have noticed that talent gap and security concerns arethe top challenges to enterprise IT organizations that want to use public cloud IaaS.

For the application area, to embrace agility, application delivery model transformation is very hotwith topics including CI/CD, microservices and containers. We use DevOps to address agilitydelivery by combining application and infrastructure. Based on Gartner’s observation, the mainchallenge in delivery model transformation is organizational readiness. In addition, many clientsleverage a platform framework to rearchitect their applications to support business transformation.However, we have not observed many successful use cases at this moment.

In this Hype Cycle release, we have added four new technologies that are emerging or being hypedin China, specifically from the Innovation Trigger to the Peak of Inflated Expectations. Thesetechnologies — RPA software, managed SD-WAN services, cloud ABI and quantum computing —demonstrate increasing impact in the China ICT market. We have renamed hyperconvergedintegrated systems as “hyperconvergence.”

We’ve also retired or suspended four technologies that are at the same speed and maturity as theworldwide situation or have been addressed by the Hype Cycle for Smart City and Sustainability inChina.

Gartner, Inc. | G00464176 Page 3 of 46

Figure 1. Hype Cycle for ICT in China, 2019

The Priority Matrix

The Priority Matrix illustrates the relative benefits and likely adoption times of relevant technologiesand services in China. Because of the technology selection criteria, the technologies in this year’supdate have mainly been of transformational, high or moderate impact during a 10-year time frame.Of those, the following innovation profiles are introducing transformational benefits to cities:

■ Machine learning, conversational AI platform and digital financial services are transformational.These initiatives are core functions used to enable the ecosystems needed to embrace extrabusiness value, but the adoption risk is also high.

■ Edge computing and Internet of Things are the extension of current platforms to drive adisruptive business case that contributes extra revenue.

■ Public cloud IaaS and DevOps can enable transformational delivery models that drive moreagile and innovative capability.


In this year’s Priority Matrix, under “years to mainstream adoption,” we moved community cloud to“moderate” from “high” benefit, and changed the years to mainstream adoption for 5G in China,NB-IoT and private cloud. This year, we added four new technologies:

■ Quantum computing and RPA software in the “high” benefit category

■ Cloud ABI and managed SD-WAN services in the “moderate” benefit category

Figure 2. Priority Matrix for ICT in China, 2019

Off the Hype Cycle

We have retired the following technologies from the Hype Cycle this year:

■ Autonomous vehicles in China

■ New-type smart city framework

■ Unified endpoint management

■ ARM servers


We dropped these technologies for one or more of these reasons. They:

■ Are trending to align with worldwide adoption and maturity

■ Have been moved to the Hype Cycle for Smart City and Sustainability in China

■ Have become obsolete

■ Were renamed

■ Have merged with new technologies

On the Rise

Corporate Compliance

Analysis By: Jie Zhang; Sandy Shen

Definition: Corporate compliance via a formal management program provides the framework forstandardizing compliance activities and lowering compliance risks. It enables a commonenterprisewide approach to corporate compliance activities that most affect the regulatory oversightof corporate governance. It is a critical component supporting decision making and authorityalignment under the supervision of the board of directors or senior management.

Position and Adoption Speed Justification: The position is not adjusted from 2018. China’sregulatory body for corporate governance is still being developed. Existing regulations are unevenand sometimes under-developed among industries. This marks the main difference in corporatecompliance practices between China and the mature economies. For example, the financialservices sector is better defined in corporate governance and compliance; but not so extensive inother areas such as information and IP protection, consumer protection, product quality and laborlaw. China is working on a number of guidelines and policies related to cybersecurity and privacy.The design and implementation of China’s Social Credit system is a part of the effort to increase themonitoring of corporate behaviors and improve compliance. The current trade dispute betweenChina and the U.S. may add pressures on Chinese businesses that operate in the U.S. and othermature markets to formally adopt programs targeting corporate compliance and oversight.

Successful Chinese enterprises have not only taken the leadership position in the domestic marketbut also started their global expansions. Yet, these local companies have trailing practices incorporate compliance management especially related to information and IP protection and cross-regional compliance management. Some of the local companies have entered partnerships withglobal businesses or acquired assets from non-Chinese companies. For those, they need toeducate themselves and expand corporate compliance to international or other regional andnational, legal and regulatory standards.

Failures of compliance management incur financial fines, reputational damages and disciplinarysanctions. In the last few years, China is rapidly developing its compliance requirements on dataprotection and privacy management. This pushes the recognition of the importance of corporatecompliance and fuses its adoptions too.


User Advice:

■ Establish a body of corporate compliance oversight (CCO) to enable compliance ownership andcharter a corporate compliance program

■ Recognize the importance of corporate compliance and build it into your overall riskmanagement practice

■ Include policy development, continuous monitoring and case management into the coreactivities of a corporate compliance program which links to the overall corporate governance(such as audit, legal, entity management)

■ Leverage regulatory content and service providers for the most up-to-date compliancerequirements

■ Invest in corporate compliance and oversight technologies to automate compliancemanagement

Business Impact: Corporate compliance management is a part of risk management. It links to auditboth internally and externally. It facilitates effective responses to investigative cases launched byauditors or regulators. Without a corporate compliance management program, businesses rely onrandom effort to fulfill compliance obligations. When managed properly and sufficiently, corporatecompliance programs can reduce various risks that are essential to business success such asoperational, reputational and legal risks. Successful corporate compliance leads to better corporategovernance. In turn, this increases trust of customers and partners. Increased maturity on corporatecompliance frees businesses to focus on new market opportunities and innovations.

Benefit Rating: High

Market Penetration: 1% to 5% of target audience

Maturity: Embryonic

Sample Vendors: Refinitiv; SAI Global; Wolters Kluwer

Recommended Reading: “Market Guide for Corporate Compliance and Oversight Solutions”

“China’s Data Privacy Standard Unfolds Measures for Its Cybersecurity Law”

Quantum Computing

Analysis By: Owen Chen; Venecia Liu

Definition: Quantum computing is a type of nonclassical computing that operates on the quantumstate of subatomic objects. The particles represent information as elements denoted as quantumbits (qubits). A qubit can hold all possible results simultaneously (superposition) until read. Qubitscan be linked with other qubits, a property known as entanglement. Quantum algorithms manipulatelinked qubits in their undetermined, entangled state. The qubits resolve to the solution when read.


Position and Adoption Speed Justification: There are various research and development areas ofquantum computing technologies. These range from semiconductor quantum computing and qubitchips to the quantum annealing process, control and readout of qubits, fault-tolerant quantumcomputing, entanglements of qubits, quantum cryptography, quantum optics, quantumcommunications, and intersections of AI and quantum computing. China’s developments ofquantum computing have been recognized in quantum communications after secure satellitecommunications were conducted in 2017. Quantum computing chips are more advanced in the U.S.than in China, so the position of this profile is lower on the Hype Cycle curve. There is much recentinterest around quantum computing, and China has been applying for patents around applicationareas of quantum computing.

Quantum computing technology continues to attract significant funding. Additionally, a great deal ofresearch is underway at universities like the University of Science and Technology of China,institutes like the Chinese Academy of Sciences, and corporate labs like Alibaba Group, Tencent,Baidu and Huawei. China also plans to open a National Laboratory for Quantum InformationScience in 2020.

User Advice: In the short term, commercial use of quantum computing will be dedicated to solvinga narrow, but important, class of problems. Several auto and aviation manufacturers and transportagencies are testing quantum computing in dedicated quantum acceleration engines for routeoptimization and traffic analysis. Other applications include image analysis, biochemistry and drugdiscovery, materials science, and code breaking (as prime number factoring). Several vendors areoffering cloud-based quantum computing or quantum as a service (QaaS). Avoid buying a quantumcomputer for on-premises deployment given the cost and ROI for exploration.

Quantum computers, in the distant future, will compromise today’s cryptographic key exchangeprotocols. Quantum safe cryptography is emerging, implemented in software, and should be amedium-term strategic initiative for organizations where data must be protected over decades.

CIOs can explore the benefits of faster computational power of quantum computing by eitherchoosing QaaS or working with vendors in your interested use case or application area.

Business Impact: Quantum computing is nascent and is not a general-purpose computingsolution.

Quantum computing could have a huge effect, especially in areas such as optimization, machinelearning, cryptography, DNA and other forms of molecular modeling, large database access,encryption, stress analysis for mechanical systems, pattern matching, image analysis and (possibly)weather forecasting. Analytics is likely to be a primary driver as the technology becomes useful, butthis is outside the planning horizon of most enterprises.


Market Penetration: Less than 1% of target audience

Maturity: Embryonic

Sample Vendors: Alibaba Group; Baidu; Huawei; Tencent


Recommended Reading: “Top 10 Strategic Technology Trends for 2019: Quantum Computing”

Cloud ABI

Analysis By: Julian Sun

Definition: Analytics and business intelligence (ABI) platform as a service — aka cloud ABI —delivers analytics capabilities and tools as a service. Solutions are often architected with integratedinformation management and business analytics stacks. These comprise database, integrationcapabilities and business analytics tools — or solutions that include only business analytics tools (toproduce reports and dashboards, for example). Most leverage autonomous, cloud-based datarepositories, but some can query on-premises data repositories directly.

Position and Adoption Speed Justification: Most organizations have either already deployed theirABI platforms in the cloud, or plan to do so. Cloud ABI or web-based analytics are the most naturalways for organizations in China to start and scale their analytics effort. Low data literacy levels ingeneral do not allow centralized IT or BI teams to open too many self-service capabilities — such asdirect data queries on data warehouses by decentralized teams. Embedded analytics as a servicehelps traditional information portals to evolve into an analytics workbench, which lets users gobeyond regular reporting. The major obstacle is that business users are not highly involved in theanalytics pipeline. Analytics solutions such as Quick BI (which relies on Alibaba Cloud) and otherlocal analytics solution in general, are bringing organizations in China to the cloud and web-basedBI solution is the mainstream.

User Advice: Organizations usually overinvest in planning on data management, rather thananalytics initiatives. However, data management is a continual and iterative project because of thegrowing volumes and types of data. Modern ABI solutions provide built-in memory engines toestablish a light analytics hub that lets business users get started quickly. To move this type ofanalytics capability to the cloud can empower the masses and achieve a quick win. The more datagravitates to the cloud as time goes by, the more value organizations can get if they plan to adoptanalytics earlier. The analytics ecosystem in China is growing with more megavendors such asAlibaba, Tencent and NetEase moving into the space. This should make it easier for IT departmentsto ease the burden on integration. Hybrid solutions that combine on-premises and cloud, ormulticloud implementations, should be seen the best-of-breed solution.

Recommendations:

■ Use the vertical strength of local vendors to build analytics solution.

■ Complement existing technology portfolios with innovative Cloud ABI offerings.

■ Use cloud ABI to pilot packaged analytics in your current information portal (such as visualinteraction) to improve data literacy.

■ Provide self-service capabilities based on the integration between cloud ABI deployment andgoverned data sources.


■ Embed cloud ABI in business processes to bring fast value to everyday tasks (such as email).

Business Impact: The lack of analytics skills continues to be a major concern for China-basedorganizations that want to be data-driven. Business users need to participate in analytics ratherthan just using data passively.

A cloud-based analytics and BI solution provides three approaches that improve how organizationscan:

■ Get faster insights: The interactive visualization that comes with cloud ABI goes beyond staticreports, and provides more obvious insights. Users with higher data literacy can start from thefirst interaction to deliver their own analysis in a familiar, browser-based environment.

■ Improve business and IT feedback: While IT provides just enough analytics capability toservice to business users, when cloud ABI is integrated into existing systems it provides anenvironment that allows business users to give feedback on how to evolve the organization’sdata capabilities. This agility could encourage better data management and sharing in theanalytics value chain.

■ Increase cross-team collaboration: Groups of teams can use existing initiatives to developand share best practice, and explore and prototype their own use cases in the cloud.

Benefit Rating: Moderate

Market Penetration: More than 50% of target audience

Maturity: Early mainstream

Sample Vendors: Alibaba Cloud; Amazon QuickSight; FineBI; Microsoft; NetEase; SAP; SAPAnalytics Cloud; Tableau Software; Yonghong Tech

Recommended Reading: “Hype Cycle for Analytics and Business Intelligence, 2018”

Privacy in China

Analysis By: Jie Zhang

Definition: Privacy in China is preserved by a national standard for privacy, a cybersecurity law andis enforced by regulations. Personal information for Chinese citizens is defined to include twocategories: general and sensitive. The Privacy Standard and the Chinese Cybersecurity Lawoutlined special data protection practices on both types of personal information. For example,locally storing private data collected in China or data localization is a mandate.

Position and Adoption Speed Justification: The position is adjusted from 2018 as the drivers forhype continue to exist. The rapid adoption of digital business in China (especially mobile payment,online shopping, and news and information content platform) continue to add risks for personal datato be hacked or mishandled. The overall trailing privacy practice in society, fraud activities,excessive data collecting and trading, etc., have driven Chinese lawmakers and regulators toestablish guardrails for protecting its citizens’ privacy. The government established its first Privacy


Standard in May 2018, enacted the first Cybersecurity Law in June 2017 and subsequently drafteda regulation on cross-border data transfers. In early 2019, China also finalized its “Guidelines forInternet Personal Information Security Protection” offering additional detailed privacy requirements.These guardrails have a direct impact on how both global and local businesses operate in China.Reactions to Chinese privacy-related topics peaked immediately after the enactment of the law(demonstrated by media and business interest, and Gartner client interactions). Cases of severepenalties have been reported on mishandling or insufficient protection of personal information sincethese privacy management requirements have been effective. The clarity provided by the PrivacyStandard and the higher pressure on the enforcement of compliance have created urgencies onbusiness to pay attention to privacy management. However, it will take time for the overall (lack of)privacy culture and laws to mature.

User Advice: Security and risk management leaders should:

■ Classify and map data between operations in China and other corporate locations, as it is thenecessary first step to prepare for data localization.

■ Incorporate a privacy assessment for the Chinese privacy standard in enterprise informationgovernance by treating it as a parallel, but complementary, effort to General Data ProtectionRegulation (GDPR) and other privacy compliance requirements.

■ Continuously monitor the development of future guidelines relevant to privacy by working withlegal experts that specialize in the Chinese market with strong privacy practices.

■ Treat the Chinese Privacy Standard within the context of the Chinese Cybersecurity Law asprivacy requirement is not only an integral part of the law but also guided and enforced by thelaw in reality.

■ Separate data protection and retention for China operations from global information governanceas local privacy rules could often be enforced differently from other regions.

Business Impact: Defining and addressing privacy compliance needs support business goals andmarket access especially for businesses in highly regulated sectors such as financial services ormultinational operations expanding in China. Therefore, global business leaders need to rethink theirmarket growth strategy. Additional investment is necessary, or a path of China market strategy isneeded as potentially the privacy risks outweigh business benefits; because costs for new ITinfrastructure, application architecture, data management and skills will rise. Although the centralgovernment and industry-specific agencies continue working on defining details on privacyrequirements, compliance risks and potential penalties for violations in China are real and can besignificant. For businesses serving the China market, privacy leaders need to consider inserting newroles, controls and policies to manage Chinese privacy requirements. Near-term privacymanagement changes from various Chinese authorities (i.e., industry-specific agencies) are alsoexpected. Therefore, privacy in China has an ongoing impact on monitoring and security audit/assessment as well.




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http://news.sina.com.cn/o/2018-05-18/doc-iharvfhv0883552.shtml

Maturity: Adolescent

Sample Vendors: BigID; Nymity; OneTrust; SAP

Recommended Reading: “China’s Data Privacy Standard Unfolds Measures for Its CybersecurityLaw”

“Security Assessment Becomes Prerequisite for Transmitting Data Out of China”

“Address Chinese Cybersecurity Law With This Playbook”

At the Peak

DevOps

Analysis By: Manjunath Bhat; Kevin Ji

Definition: DevOps is a customer-value-driven approach to deliver solutions using agile methods,collaboration and automation. It emphasizes people and culture to improve collaboration betweendevelopment, operations and other stakeholders to navigate uncertainty and accelerate the deliveryof customer value. DevOps implementations use architecture and tools to improve the flow of work.

Position and Adoption Speed Justification: Gartner has seen a 50% YoY increase in the numberof DevOps inquiries from China. The nature of inquiries has changed from seeking to understandwhat DevOps is to now ensure that organizations have the right culture, practices and tools in placeto realize value from DevOps initiatives. Most organizations in China differ from their counterparts inthe western markets in their approach to DevOps in that DevOps is often confused as beingsynonymous with automation or adoption of modern cloud-native technologies such as containers,microservices or software-defined infrastructure. DevOps thus becomes a technology-firstdiscussion with the focus primarily on enabling faster delivery using CI/CD.

Organizations in China have traditionally had rigid hierarchies and organizational structure thatdefined their day-to-day operational work. So, a cultural transformation that requires aligningbusiness, dev., QA, security and operations teams using a product line operating model is seen asvery disruptive. On the other hand, Organizations in China have been ahead of their westerncounterparts in their approach to rapid experimentation even when they did not explicitly refer to itas being “agile.” The startups and digital native companies in China adopt a culture of “fail-fast,”“fail-early” that is at the heart of DevOps — this culture is now starting to influence the moretraditional companies in existing verticals such as manufacturing, BFSI, utilities and governmentagencies.

The rapid digital transformation in the country has led to organizations adopting open sourcetechnologies in a big way. However, a dearth of open-source skills and talent poses an immediatechallenge. Adopting open-source technologies, such as Docker, Kubernetes, Jenkins, Chef, Ansibleor the Elastic Stack (monitoring), requires modernizing legacy applications and this is where thetalent shortage becomes acute.


Gartner sees DevOps initiatives in China align with the broader adoption of cloud services (private/public), microservices and the shift from project to a product line operating model.

User Advice: Successful adoption of DevOps and agile development methodology require anorganizational cultural and philosophy shift, which is not easy to change, particularly in China asorganizations tend to have rigid organizational structures.

Establish an adaptive and responsive culture by selecting DevOps leaders with a team-orientedmindset focused on enabling collaborative learning teams with shared goals and responsibilities.

Create an open-source talent pool by hiring and training individuals in open-source technologiesand behaviors, such as community mindset and continuous learning. Automate open-sourcegovernance by implementing software composition analysis (SCA) and binary repository tools forsecurity and license compliance.

Select personal focus areas from the following: agile practices, automation, cloud operations,platform operations and service delivery. Determine the specific platforms, providers and tools usedin your focus areas, and start experimenting and training with them to gain familiarity. Learn codingand software deployment skills. Formalize a personal skills development plan to bridge gaps inknowledge, based on your target role and focus areas. Request time and budget to train andpractice on key tools.

Establish dedicated platform teams by recruiting diversified subject matter experts (SMEs) who willpromote agility and responsiveness to product teams. Redefine the platform as a set of productsthat continuously evolves to fulfill developer needs by building a collaborative product mindset.

Business Impact: DevOps initiatives must begin with identifying the business justification. Placingcustomer value at the core of DevOps results in driving right behaviors. DevOps teams must focuson customer business outcomes, rather than their own individual metrics. The success of the teamand of every individual is assessed by the degree to which the business metrics, business processrequirements and customer needs are being met.

Faster cycles times and realization of business value are the most frequent organizational outcomesfrom DevOps. Organizations must not use cost reduction as the primary driver for DevOps — costswill indirectly reduce due to improvement in process efficiency, collaboration and automation.However, starting with reducing costs will be counter-productive. Organizations can expect in anincrease in initial costs due to reskilling and retooling of processes.

Gartner views DevOps as transformational because it accelerates an organization’s journey to digitalbusiness. DevOps invariably brings IT closer to business and the customer due to rapid feedbackloops and closer collaboration thus enabling faster shortening the time an idea takes to go frominception to realization.

As a consequence, Gartner expects the emergence of DevOps toolchain providers that specificallyaddress the unique needs of the China market in 2020 and beyond.

Benefit Rating: Transformational




Sample Vendors: Atlassian; CloudBees; Datadog; FOSSID; Octopus Deploy; Red Hat; Selenium;Snyk; Splunk

Recommended Reading: “How to Navigate Your DevOps Journey”

“Adopt an Iterative Approach to Drive DevOps Success in Large Organizations”

“Three Ways Midsize Enterprises Can Maximize Value From DevOps”

“Four Steps to Adopt Open-Source Software as Part of the DevOps Toolchain”

“DevOps Success Requires Shift-Right Testing in Production”

“Avoid Failure by Developing a Toolchain That Enables DevOps”

“How to Scale DevOps by Building Platform Teams”

“Top SRE Practices Needed by Teams Scaling DevOps”

AIOps Platform

Analysis By: Peter Liu; Kevin Ji

Definition: Artificial intelligence for IT operations (AIOps) platforms combine big data, AI/machinelearning and other technologies to support all primary IT operations functions with proactive,personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources,data collection methods, analytical technologies (real-time and deep) and presentationtechnologies.

Position and Adoption Speed Justification: Growing demand for AIOps platform capabilities isfueled by the need to automate more and more IT operations functions as roles and responsibilitiesconverge (with DevOps as a leading example) in the pursuit of greater agility. These IT operationsfunctions include data-at-rest (aka historical) and data-in-motion (aka real-time) data analysis. Thisrequired degree of automation necessitates continuous insights derived from machine learningalgorithms based on data-generated ITOM disciplines, such as APM, ITIM, NPMD, DEM and servicemanagement.

Adoption of AIOps platforms — in particular, machine data, event, telemetry and log-management-oriented tools — continues to rise in support of monitoring and root cause analysis efforts. This isdue to their ability to rapidly perform and support highly complex diagnostic tasks across multipledomains.

Interest and investment in AIOps platform technologies in China will continue to grow due to:

■ Growing demand for increasingly proactive IT operations


■ The speed, scale and complexity resulting from multicloud and hybrid cloud and digitizationstress traditional rule-based performance monitoring and management

■ Rising data diversity, cost optimization goals and quality expectations

■ Continued data growth, dynamism and related complexity in IT operations

■ Requirements for autonomous governance (part of adaptive governance) to enable businesssustainability

Similar to AIOps adoption worldwide, adoption in China is tempered by broad “analytics” marketconfusion, intentional obfuscation by vendors and the rising costs associated with ever-increasingdata volumes. In addition, China faces its own challenges in AIOps adoption. For example, themajority of vendors in China take a modular approach rather than a platform-centric approach. Thisrequires users to supplement capabilities with data ingestion technology, which most users lack. Inaddition, due to regulations, heterogeneous system architectures, skill sets and cultural issues,CIOs in China are able to achieve only a small portion of AIOps, such as monitoring and analytics.

The following trends are emerging in this space:

■ Vendors outside the AIOps market, including APM solution providers, are expanding into theAIOps market in hopes of broadening their market opportunity.

■ AIOps solutions are beginning to offer capabilities to triage problems and direct their resolutionvia integration with run book automation and application release orchestration (ARO) tools.

■ Some vendors are pursuing an “open box” approach, allowing the algorithms to be accessiblefor user modification, while the majority of vendors are pursuing a “closed box” approach,disallowing user interaction.

User Advice: I&O leaders must build and implement a strategic AIOps platform investment plan thatsupports multiple, major IT operations functions (such as performance analysis, experiencemanagement and delivery automation) and incorporates:

■ Prioritization of high-value use cases across all IT operations management

■ Inventory of existing skills and tooling capabilities across all IT operations management

■ Incentive and organizational changes to drive collaboration between the operations team anddata science team

■ Leveraging of existing AIOps platforms so the focus can shift toward data sources and thepresentation layer

■ Development of an AIOps strategy with an eye on (1) balancing ease of implementation/use withinterchangeability of platform capabilities; (2) IT operations management tool portfoliorationalization; and (3) key technology gap investment

■ Staged implementation of AIOps capabilities


An architected AIOps strategy can serve as a useful mechanism for focusing both skills and toolinginvestments on an ongoing basis. Enterprises that already have various AIOps platforms in the formof stand-alone products or as capabilities in domain-centric tooling must routinely audit theircapabilities. Few I&O teams have the skills or vision needed to take full advantage of all thecapabilities of the AIOps platforms they already have. Organizations that are able to take advantageof these capabilities rarely have incentives in place to share and grow the necessary skills; thus,both areas should be addressed as soon as possible.

Business Impact: By enabling I&O teams to enhance and transform major operational functionswith a real, automated insight generation capability, organizations across all verticals stand torealize:

■ Agility and productivity gains via active analysis of both IT and business data, yielding newinsights on user interaction, business activity and supporting IT system behavior.

■ Service improvement and cost reduction via a significant reduction of time and effort requiredto identify the root cause of availability and performance issues. Behavior-prediction-informedforecasting can support resource optimization efforts.

■ Risk mitigation via active analysis of monitoring, configuration and service desk dataidentifying anomalies from both operations and security perspectives.

■ Competitive differentiation/disruption via superior responsiveness to market and end-userdemand based on machine-based analysis of shifts, beyond those that are immediately obviousto human interpretation.



Maturity: Emerging

Sample Vendors: Alibaba Cloud; AsiaInfo; Baidu; BigPanda; CloudChef; Cloudwise; EOITek;Huawei; LinkedSee; Tingyun

Recommended Reading: “Market Guide for AIOps Platforms”

“Use AIOps for a Data-Driven Approach to Improve Insights From IT Operations Monitoring Tools”

“Predicts 2019: IT Operations”

Conversational AI Platform

Analysis By: Tracy Tsai; Adrian Lee

Definition: Conversational AI platforms can be used by developers to build conversational userinterfaces, chatbots and virtual assistants for a variety of use cases. They offer integration into chatinterfaces such as messaging platforms, social media, SMS, websites or similar. A conversationalplatform has a developer API so third parties can extend the platform with their own customizations.


Position and Adoption Speed Justification: Conversational platforms in China were initially usedfor messaging-style chatbots, which are mostly rule-based. However, the use of conversationalplatforms is rapidly evolving to handle more complex customer-experience-oriented tasks such ascustomer service, digital commerce and travel booking — moving from pure tech deployment torefining customer experiences and cost reduction. The rule-based approach is only suited to linearor noncomplex customer interactions. Currently, the approach is shifted toward natural languageunderstanding (NLU) using recurrent neural networks (RNN), generative adversarial networks (GANs)or specific long- or short-term memory RNNs as an example to better understand users’ intent. Inaddition to NLU, localized relevant content, domain knowledge graphs and business logic fordialogue management are critical to support a conversational platform. Most Chinese speech-to-text (STT) vendors such as Baidu, Alibaba Group (AliGenie) and iFLYTEK can support multipleChinese dialects and accents. However, the mature use cases of STT are more limited to“command” type applications, such as in virtual personal assistants or smart speakers. Chinesenatural language processing (NLP)-based conversational platforms will require at least five to 10years to mature as NLU, domain knowledge graphs and algorithmic training models based onlocalized languages will need time.

User Advice: Enterprise architecture and technology innovation leaders planning to deployconversational platforms should consider the following factors in evaluating and selecting vendors:

■ Identify your business objectives and what kind of incremental value conversational platformswill bring to support the organization’s objectives.

■ Collaborate with line-of-business stakeholders on how to scope your conversational platform’srequirements, features and experiences.

■ State your requirements during preliminary vendor qualification to ensure that AutomaticSpeech Recognition (ASR), STT, Text to Speech (TTS), NLP, domain knowledge graphs andease of use are offered. Only STT and keyword-/rule-based NLP do not provide naturalconversational experiences.

■ Request vendors to provide inference model for testing at least 30 or up to 90 days with yourown company data. Each model needs to be trained and optimized with the new set of data.

■ Be prepared to change to or add new providers of conversational platform applications. Ensurethe datasets and business logic can be retrained and transferred seamlessly to the next vendor.

Business Impact: The initial business value of a conversational AI (CAI) platform for enterpriseswas to improve customer experience. Other benefits came from improving operational efficiency viacost reduction in call centers or increase in sales transactions. The NLP-based CAI allowsenterprises to maintain and improve the conversational AI applications more effectively than rule-based and keywords search approach. Gartner has observed an increase in the number of inquiriesregarding conversational AI platforms to support enterprise employees and improve productivity inChina. Finally, there are new revenue models created by voice-enabled conversational commercethrough retail apps, smart speakers or home appliances. Voice-enabled conversational platformsremain elusive in most use cases, especially for virtual assistants, as consumer questions are toounstructured and generalized. In addition, the lack of relevant services to support revenue-


generating use cases is another issue. Chinese enterprises cannot delay the deployment ofconversational platforms for customers or employees or face the risk of losing their brand relevanceto customers, as well as operational competitiveness.




Sample Vendors: AISpeech; Alibaba Group; Baidu; Emotibot Technology; iFLYTEK; IBM; Microsoft;Tencent; Zhuiyi Technology

Recommended Reading: “Market Guide for Conversational Artificial Intelligence in China”

Edge Computing

Analysis By: Evan Zeng; Kevin Ji

Definition: Edge computing describes a distributed computing topology where informationprocessing is placed close to the things or people that produce and/or consume that information.Edge computing is used to keep traffic and processing at network edge and off the centralizedcloud or data centers.

Position and Adoption Speed Justification: Most of the technology for creating the physical andvirtual infrastructure of edge computing is readily available, but widespread application of thetopology and viable business cases are not yet developed materially in China. Also, 5Ginfrastructure development has been accelerating in China, and major Chinese carriers have theirplans to rollout multiaccess edge computing infrastructure. But these are all still under constructionand are not yet used for any commercial services.

There are some use cases proliferate, such as those in the IoT domain for the edge as a topologicaldesign pattern to support IoT applications and those in mobile gaming domain to leverage themultiaccess edge infrastructure to offload intensive computing tasks and build low-latency featuresto enhance gamer experience. Application, data, infrastructure systems and security will need to bestretched to include edge locations and edge-function-specific technologies such as data thinning,software defined perimeter, video compression and edge analytics, etc.

User Advice: By far, IoT contributes most use cases for edge computing with IoT applicationsrequiring edge infrastructure to provide near-real-time interactions between people and/or thingsand reduce network cost for data transmission. However, much more use cases outside of IoTdomain are fast developing, such as mobile gaming, ultra-low or low latency applications such asVirtual Reality/Augmented Reality/Mix Reality applications and high accuracy remote operation.

I&O leaders responsible for planning and enabling infrastructure delivery should:

■ Address emerging digital business requirements by not only including edge computing as a partof your infrastructure strategy but also defining your edge requirements.


■ Prepare in-house skill sets for edge computing by identifying skill and organizational gaps.

■ Investigate your technologic architectural gaps such as security, network and management inadopting edge computing.

■ Plan your pilot use cases of adopting edge computing by starting with well approved businesscases such as preventative maintenance in manufacturing industry to accumulate developmentand operational expertise.

■ Keep an eye on 5G network development in China and include multiaccess edge computinginto your planning of adoption on edge computing.

Business Impact: Edge computing solves some pressing issues such as unnecessary WAN costsand unacceptable latency. It also enables ultra-low latency application scenario that requires lessthan 10 ms latency from things to edge infrastructure, such as autonomous driving. The edgecomputing topology will enable the specifics of digital business and IT solutions uniquely well in thenear future.




Sample Vendors: Alibaba Cloud; Amazon Web Services; Baidu; China Mobile; China Telecom;China Unicom; Huawei; Tencent Cloud

Recommended Reading: “Market Guide for Cloud Infrastructure as a Service, China”

“Market Guide for Edge Computing Solutions for Industrial IoT”

“Exploring the Edge: 12 Frontiers of Edge Computing”

5G in China

Analysis By: Peter Liu

Definition: 5G is the next-generation cellular standard after 4G (LTE, LTE-A and LTE-A Pro). It hasbeen defined across several global standards bodies — International Telecommunication Union(ITU), 3GPP and ETSI. The official ITU specification, International Mobile Telecommunications-2020(IMT-2020 Standard), targets maximum downlink and uplink throughputs of 20 Gbps and 10 Gbps,respectively, latency below 5 milliseconds, and massive scalability. New system architectureincludes core network slicing, as well as edge computing.

Position and Adoption Speed Justification: According to GSA, as of April 2019, 39 operatorshave announced a 5G technology in their network. This is less than 5% of mobile networks(excluding MVNOs and subbrands) and includes both 3GPP-compliant and noncomplianttechnology. Fifteen have launched FWA.


https://gsacom.com/paper/lte-5g-market-statistics-8-april-2019/

The 3GPP’s Release 15 was frozen on June 2018, with commercial network infrastructure based onthe earlier New Radio (NR) specification launched by the end of 2018. NR allows CSPs to launch 5Gwith only new radio access network (RAN) deployments, leaving the existing core intact. 5G coreand edge topology also need to be added to realize the full benefits of 5G. This may occur latertoward 2022 to 2025.

Examples of early CSP commercial 5G deployments include those in the U.S. (AT&T and Verizon),South Korea (KT, LG U+ and SKT), the Middle East (various CSPs) and Australia (Telstra). Majorchallenges include terminal availability, coverage, and clear 5G use cases and business models.

Chinese government policy is to support both the development of 5G standards and thecommercial deployment of 5G networks through a range of policies and initiatives, includinggovernment support for research and development. The Chinese government allocated 5Gspectrum in the midband frequency range to three state-owned CSPs, preparing the way for large-scale network testing in 2019. Both China Telecom and China Unicom received 100 MHz in the 3.5MHz band, while China Mobile obtained 260 MHz of spectrum in the 2.6 GHz and 4.8 GHz bands.Confirming the frequency bands will enable CSPs and equipment providers to determine productdevelopment goals and accelerate the process. The Chinese government granted 5G licenses to thecountry’s three major telecom operators and China Broadcasting Network on 6 June 2019, givingthe go-ahead for full commercial deployment of the next-generation cellular network technology.

China is well-equipped for 5G, thanks to factors such as widespread fiber availability, LTE maturity,supportive government policies, and leading equipment and device manufacturers. The established5G ecosystem is poised to create strong 5G demand in China. However, early 5G networks willmost likely be served as a hot spot technology to supplement existing LTE in Tier 1 cities. It will taketwo to five years to reach nationwide coverage.

User Advice: CSPs should:

■ Focus mobile infrastructure planning on LTE, LTE-A, LTE-A Pro, small cells and heterogeneousnetworks (HetNet) as part of a planned transition toward 5G. 5G will coexist with thosetechnologies for many years.

■ Test backward compatibility to preceding-generation (LTE) devices. This is necessary becauseinitial 5G coverage may be limited, so new devices need to be able to use at least 4Ginfrastructure as a fallback. 3GPP is looking only at 4G/5G interoperability; IMS providers will berequired to handle additional intergeneration interwork for 5G.

■ Focus on related architecture initiatives — such as software-defined network (SDN), networkfunction virtualization (NFV), CSP edge computing and distributed cloud architectures, as wellas end-to-end security in preparation for 5G. 4G mainly adopts cellular network architecture,but 5G will prove more complicated, and a heterogeneous network will be commonly adopted,with a denser grid in hot spots, so topology changes must be planned. Operations will needfurther automation and orchestration at scale as well, so self-organizing network (SON)frameworks need to be in place.

Enterprises should:


■ Plan on using widely available standards-based 5G equipment or services before 2020. Factorin the potentially higher total cost of ownership (TCO) of using a nonstandard offer or device.

■ Clearly define use cases that will benefit from 5G’s unique performance characteristics bydocumenting use-case or application performance requirements. Use legacy services andtechnologies to support all other use cases through at least 2022.

■ Question potential price increases for future 5G plans by notifying carriers that the enterprisewill give purchase preference to providers that do not charge a premium for 5G data service thatsupports legacy applications.

Business Impact: 5G requirements cover primarily three technology aspects:

■ Enhanced mobile broadband (eMBB)

■ Ultrareliable and low-latency communications (URLLC)

■ Massive machine-type communications (mMTC)

URLLC and mMTC will be implemented after eMBB. Only eMBB addresses the traditional mobilehandset requirement of ever-higher throughput. URLLC addresses many existing industrial, medical,drone and transportation requirements — where reliability and latency requirements surpassbandwidth needs. Finally, mMTC addresses the scale requirements of IoT.

We believe the first application will focus on eMBB and mobile edge computing that includes media,gaming and AR/VR.

We also believe 5G may focus more on enterprise applications compared with previous generations.B2B and B2B2C models may become more popular. Key industries in the beginning will includegovernment (smart city), utilities (smart grid) and entertainment (gaming, video and AR/VR).



Maturity: Emerging

Sample Vendors: Cisco; Ericsson; FiberHome Telecommunication Technologies; Huawei; Intel;Mavenir; Nokia; Qualcomm; Samsung; ZTE

Recommended Reading: “Market Guide for CSP Edge Computing Solutions”

“Market Trends: Make Compelling 5G Technology Selections and Be First to Attain 5G Success”

“IT Market Clock for Communications Service Provider Infrastructure, 2018”

“Magic Quadrant for LTE Network Infrastructure”

“3 Requirements to Successfully Offer Commercial 5G Services”


“Exploit the Innovation Opportunities Enabled by Future 5G Wireless”

RPA Software

Analysis By: Roger Sheng; Cathy Tornbohm

Definition: Robotic process automation (RPA) software is combined by user interface recognitiontechnologies and business process execution. It can simulate the human operations’ (such asmouse clicks and keystrokes) drive applications and process execution work. Sometimes it can bedesigned for application to application automation as a software-based robotic operation. It is atype of automation that needs structured data for working process.

Position and Adoption Speed Justification: The RPA can reduce the human workload on a routingwork process, which is rule-based and repetitive rekeying or data collation. Insurance and financialindustry companies are the largest users in China, which is similar as the global situation. RPA canbe used for accounting or client data collection, which requires human operation heavily as a“digital labor” to reduce both labor cost and the mistakes in the process. On the other side, RPAdoesn’t work for frequent process/rule change scenarios. Also, a robust system with strongrecovery capability is required to roll back or recover any exception if there is an execution accident.

Internet e-commerce companies also are the major users for RPA because the massive volume oftrading data only can be processed by automatic process. Alibaba Cloud developed an RPAsolution based on its internal RPA development experience and promotes it to outside cloud clients.

The major barrier for RPA in China is the software development capabilities of the end users.Usually there are not enough IT engineers engaged in business operation and process in traditionalChinese enterprises, which is important to RPA development. Recently, thanks to the hype of digitalbusiness, Chinese companies see RPA as one important solution for digitalization for the operationprocess. Also, the adoption of AI technologies can improve the data collection accuracy, whichsupports more structured RPA solutions.

User Advice: Most RPA solutions include screen capture and working process automation, andsome will use AI for data collection and process decisions. The end users need to integrate the RPAin their systems throughout the execution process to achieve the returns of automated work.

Currently, there are many examples to prove that RPA can develop automation capabilities andreduce the labor workload by software tools. In China, enterprises should consider using RPA bythe following factors:

■ RPA tools can eliminate keying errors.

■ Automated work is time-stamped, trackable and auditable.

■ RPA software can be cheaper than increasing labor cost.

■ RPA can reduce the employee management issues.

■ RPA tools can be designed to operate 24/7.


■ User interface recognition is less able to work in complicated system environments.

The labor-intensive traditional industry enterprises should consider RPA but it is not a completesolution. End users should consider developing RPA with their BPO and IT suppliers, which havebuilt RPA tools. The existing process automation should be incorporated with both processtransformation and digitalization. For digital-business-oriented enterprises, RPA should bedeveloped by working with business flow partners to ensure the structured data and process outputsync. The selection of RPA vendors should consider flexibility, stability, compatibility, maintenanceand system costs.

Business Impact: For the enterprises that have huge demand on data work, which rely on heavilyreplicated labor work, RPA can be the effective solution to reduce the need of employees andincrease the working efficiency. It can lead to the lower cost for all process operations. Theenterprises can design the process automation for digital business outcomes and develop newbusiness models. Especially in the digital transformation, RPA software will take an important role tooptimize the business process operation.



Maturity: Emerging

Sample Vendors: Alibaba Cloud; Automation Anywhere; i-search; Leanpro Tech; UiPath

Recommended Reading: “Robotic Process Automation: Eight Guidelines for Effective Results”

“Market Guide for Robotic Process Automation Software”

“Strategies for Effective Application of Robotic Process Automation”

“When and How to Use Robotic Process Automation in Finance and Accounting”

“Solution Comparison for Three Robotic Process Automation Vendors”

Machine Learning

Analysis By: Melody Chien

Definition: Machine learning is a technical discipline that aims to extract certain kinds of knowledgeand pattern from a series of observations. There are three major subdisciplines, which relate to thetypes of observation provided: supervised learning, where observations contain input/output pairs(also known as “labeled data”); unsupervised learning (where labels are omitted); and reinforcementlearning (where evaluations are given of how good or bad a situation is).

Position and Adoption Speed Justification: In China, artificial intelligence (AI) is regarded as amajor national economic strategy for the next decade by the Chinese government. As the coretechnology of AI, machine learning is one of the hottest technologies at the moment, given its


extensive range of effects on business. Backed by strong government mandates, incentive plansand billions of dollars in both private and public investment, China has made significant progress inmachine learning technology in recent years. It estimates the data science platform market to beover $76.3 million of revenue in 2018 in greater China region. This reflects significant growth of31.7% compared with 2017, which is a good proxy for the growing use of ML (see “Market Share:All Software Markets”), due to increased digitalization.

Seen as disruptive force, machine learning technology has been used in many industries in China tocreate competitive advantage for better products, or services. For examples, the fraud detection inbanking industry and predictive analysis in equipment maintenance in manufacturing industry. Manytechnology providers in China offer industry-based solution with prepackaged algorithms inmachine learning technology. The drivers of continued massive growth and adoption are thegrowing volume of data and the complexities that conventional engineering approaches areincreasingly unable to handle. In the future, advances in transportation, energy, medicine andmanufacturing will be impossible without machine learning.

User Advice: Start with simple business problems for which there is consensus about the expectedoutcomes, focus on a quick win and gradually move toward complex business scenarios. Nurturethe required talent for machine learning, and partner with universities and ML service providers tokeep up to date with the rapidly changing pace of advances in data science. Evaluate thecapabilities of machine learning and its potential business impact across a wide range of use cases— from process improvement to the development of new services and products. Focus on data asthe fuel for machine learning by adjusting your data management and information governance formachine learning. As data is a unique competitive differentiator, it’s essential to consider the propersize of the training dataset and the quality of it, and any possible human bias. Although the choiceof machine learning algorithms is limited, data sources are abundant and a good long-terminvestment.

To facilitate the adoption of machine learning technology, it’s encouraged to consider cloud-baseddata science/ML tools to minimize the upfront setup. The vendors of these tools also provide off-shelf prebuilt and pretrained data models and algorithm (such as image recognition and naturallanguage processing). As market and technology matures, many more of these ML products will beavailable to meet various use cases. When selecting these ML products, it’s important to make surethat vendors provide adequate information about the products such as any potential bias in data.

Many organizations struggle when it comes to systematically productizing machine learning results,as the production process is either overlooked or left solely to the DevOps team. Machine learningoperationalization is a critical step — the “last mile” toward business value (see “How toOperationalize Machine Learning and Data Science Projects”). Organizations should maximizeoperationalization success with close collaboration among domain experts, process engineers, ITprofessionals and business practitioners, in addition to existing data science talent.

Business Impact: Machine learning drives improvements and new solutions to business problemsacross a vast array of business and social scenarios:

■ Automation


■ Drug research

■ CRM

■ Supply chain optimization

■ Predictive maintenance

■ Operational effectiveness

■ Workforce effectiveness

■ Fraud detection

■ Automated vehicles

■ Resource optimization

Machine learning impacts can be explicit or implicit. Explicit impacts result from machine learninginitiatives. Implicit impacts result from products and solutions that you use without realizing theycontain machine learning.




Sample Vendors: Alibaba Group; Huawei; IBM; SAP; SAS; Tencent; Transwarp

Recommended Reading: “Artificial Intelligence Primer for 2019”

“Magic Quadrant for Data Science and Machine Learning Platforms”

“Critical Capabilities for Data Science and Machine Learning Platforms”

“How to Choose the Right Data Science and Machine Learning Platform”

“AI Governance Spotlight: Early Lessons and Next Practices”

Managed SD-WAN Services

Analysis By: Evan Zeng

Definition: Managed SD-WAN services include the SD-WAN product, WAN transport andmanagement, and are fast emerging in China. Such providers include NSPs, carriers, cloudproviders, OEMs with service arms and network SIs/VARs. They operationally manage customers’SD-WAN products (physical appliances or software instances) that are either enterprise-owned orare included with the service. Overlay network with optimized internet performance is a new feature.


Position and Adoption Speed Justification: China’s telecommunication market is tightlyregulated, and its enterprise WAN service market is mainly dominated by the big three carriers(China Telecom, China Unicom and China Mobile). Such carriers are at the early stage to offermanaged SD-WAN services, partially because of the investment protection of their MPLS networks.The competition between carriers to win enterprise WAN business is a major catalyst to drive SD-WAN deployment and related managed services. Startup NSPs and public cloud providers are themajor competing forces in this market. Some of them have self-built, over-the-top networks withsmart routers to leverage carriers’ internet as WAN transport. However, noncarrier providers stillhave small share of the overall enterprise WAN market and carriers’ move to embrace managed SD-WAN services determines the pace of overall market development.

Even though enterprises’ adoption of SD-WAN technology is fast growing in China, majority ofenterprises are still on legacy hub-and-spoke WAN architecture with centralized internet access attheir data centers. Such legacy WAN and internet access architecture should be modernized in thenext few years with the adoption of SD-WAN technology and managed SD-WAN services.

User Advice: The benefits of managed SD-WAN services are not only for better agility, higherperformance and centralized management of all WAN devices, but also the WAN network costoptimization by leveraging lower-cost WAN technologies and optimized internet.

I&O leaders should evaluate managed SD-WAN services when planning to adopt SD-WANtechnologies. The capabilities of SD-WAN technology continue to grow, and managed SD-WANservice providers will enrich their services with critical new features such as WAN access security,access authentication and internet performance optimization, etc. If some of such features areimportant and cannot self-build at reasonable cost, I&O leaders should consider the adoption ofmanaged SD-WAN services.

Business Impact: Managed SD-WAN services aim to optimize enterprise WAN costs in four ways:

■ SD-WAN products facilitate easier integration and operation of often lower-cost internetservices as a WAN transport option than a traditional router.

■ Compared with routers, SD-WAN offers superior application-policy-based control — allowingcritical applications to be preferred over noncritical applications — affording enterprises moregranular control in meeting business requirements.

■ The managed SD-WAN services enable enterprises to quickly adopt these capabilities withoutenhancing skills more typical of new technology adoption.

■ The self-built backbone from managed SD-WAN service providers offer much enhancingcapabilities than enterprises’ self-built SD-WAN. For example, the optimized internetperformance can significantly reduce internet delays, which is hard for enterprises to developsuch features because it requires the deployment of many smart routers at the middle of theinternet to detect and avoid path congestion.

However, adopting such services will also bring in network lock-in to enterprises, reducing networkchoices or becoming over-relied on external managed network service providers.





Sample Vendors: Alibaba Cloud; China Mobile; China Telecom; China Unicom; Huawei; TencentCloud

Recommended Reading: “Market Guide for Managed SD-WAN Services”

“SD-WAN and NFV Bring New Managed WAN Competition”

Community Cloud

Analysis By: Evan Zeng; Kevin Ji

Definition: Community cloud refers to a multitenant offering where the subscribers have explicitlychosen to share the offering with a precertified community of organizations like governmentagencies. It is neither a fully public, nor private model. It can be offered at customer premises or atprovider premises. In China, tenants are often organized and isolated by the industry namedindustry cloud.

Position and Adoption Speed Justification: Regulators in highly regulated industries such asgovernment agencies and banking still have major concerns on high availability, security,compliance and risk control of public cloud in China and they prefer community cloud infrastructurewith certified access by only enterprises that are in the same industries and are under the sameregulator’s governance. Due to the potential high-consumption volume, hyperscale cloud providerswant to access this market, but regulators prefer providers that have established operational andcompliance expertise in specific industries and are under their regulation. Hyperscale cloudproviders such as Alibaba Cloud and Tencent Cloud are not under any regulation of state council orChina’s Banking and Insurance Regulatory Commission (CBIRC), thus they don’t fully meet suchrequirements. Providers such as Pingan Cloud and CIB Fintech get favored in financial serviceindustry because they are under the regulation from CBIRC. Similarly, providers such as InspurCloud and China Telecom get favored in government cloud projects because they are under theregulation of China’s state council. Providers are progressing their product offerings at much slowerspeed than those at hyperscale public clouds.

Industries outside of highly regulated industries do not have such limitation on cloud providers andit is an open market for competition. But community cloud makes less sense in such industries andif there are some, the size each one is normally not big.

User Advice: We have observed that there are two scenarios where many clients leveragecommunity clouds to deliver business value in the China market:

■ In highly regulated industries, clients have built a dedicated compliance cloud service for theenterprise and its partners.


■ Inside large conglomerates, clients have built community clouds to drive shared services acrossdifferent subsidiaries. This is useful because there are so many conglomerates in China. Theirsize is so big to justify building community cloud and continue to take full-stack control.

CIOs in highly regulated industries:

■ Evaluate community cloud for vertical applications or sensitive workloads when public cloud isunable to offer the same level of industry regulatory compliance or unable to achieve therequired level of security and governance.

■ Position community cloud services as an alternative way of sourcing to achieve bettercompliance, cost savings, service agility and IT service innovation.

CIOs in other industries:

■ Evaluate community cloud for mission-critical workloads or sensitive workloads when publiccloud is considered not fit to your requirements of compliance, security, data privacy orgovernance.

■ For large conglomerates, evaluate community cloud for implementing shared-service strategyacross all your subsidiaries to achieve better IT efficiency and agile service delivery.

Business Impact: Community cloud is being increasingly adopted in enterprises because it canprovide the following business benefits:

■ Security — Community cloud is not entirely public and can has more access restriction to limittenants for their access. It is considered to be less exposed to security risks than public cloud.

■ Industry regulatory compliance — Public cloud does not customize its cloud platforms byindustry regulation, but industry-based community cloud does. This can help tenants to easilyachieve industry regulatory compliance.

■ Business agility — In comparison to internal private cloud, community cloud can provide betteragility to tenants, although less agility than public cloud.

■ IT efficiency — Building an internal private cloud is a high-cost activity. Leveraging existingcommunity cloud is a lower-cost approach if enterprises do not have mature skill sets to governpublic cloud adoption.




Sample Vendors: Alibaba Cloud; Baidu Cloud; China Telecom; China TravelSky Holding Co.; CIBFintech; Inspur; Pingan Cloud; Tencent Cloud

Recommended Reading: “Industry Vision: Transition Business Models by Adopting ChineseEnterprises’ Industry Cloud Strategies”


“Market Guide for Cloud Infrastructure as a Service, China”

Sliding Into the Trough

Internet of Things

Analysis By: Milly Xiang

Definition: The Internet of Things (IoT) is the network of dedicated physical objects that containembedded technology to communicate and sense or interact with their internal states and/or theexternal environment. IoT comprises an ecosystem that includes assets and products,communication protocols, applications, and data and analytics. IoT is a core building block fordigital business and digital platforms.

Position and Adoption Speed Justification: Gartner’s CIO Survey 2019 shows that IoT is regardedby CIOs as one of the top four game-changing technologies. IoT is widely perceived as the essentialcomponent for digital business optimization and transformation across all industries. In themeantime, the deployment of IoT is not purely about technology, it requires strong alignment with anenterprise’s business vision and objectives. Gartner’s 2018 IoT Strategies Survey indicates thatcreating a digital platform or ecosystem to support partners and customers, improving productquality and design, and improving customer or user experience are the top three business driversfor IoT initiatives.

IoT adoption in China is proliferating, largely because companies often find that IoT investmentdelivers good business outcomes. Gartner’s 2018 IoT Strategies Survey shows that 80% of Chineseorganizations found that IoT delivered more than planned outcomes. But IoT involves complicatedtechnology stacks ranging from IoT-connected things, IoT edge computing and an IoT coreplatform, as well as one or more business applications. Thus, IoT drives profound changes to corebusiness applications and distributed IT infrastructure, which requires Chinese enterprises tocontinuously invest in the requisite skills needed to support IoT-enabled applications.

Chinese enterprises increasingly recognize that the factors driving true proliferation of IoT intoenterprises is not simply technology maturity. It also requires considerations on business (linking IoTadoption and integration with business and financial performance) and organization (having the rightskills and aligning with how IoT is used) dimensions. We are expecting to see that Chineseorganizations increasingly align IoT strategies with their digital business innovation efforts. Thisenables them to identify the technologies and applicable use cases that will impact their business ina positive way, while ensuring they have the skills and partners to deliver them when required.

Due to the broad and fragmented nature of IoT, deployment and maturity levels differ in use casesand verticals across consumer, commercial and industrial sectors. We are expecting thatcommercial and industrial sectors where visible business outcomes can be generated will enjoyfaster penetration of IoT, while the consumer sector will remain hardware-driven while exploringviable business and monetization models.


User Advice: For Chinese companies that want to implement IoT technologies for their digitalbusiness optimization and transformation, Gartner has the following recommendations:

■ IoT trends and emerging technologies will drive digital business innovation for a decade. CIOsmust identify and evaluate the IoT trends and technologies that either will drive innovation inyour industry and organization or will create new risks. Implement the technical andorganizational changes required to address the risks and opportunities.

■ Exploiting IoT trends to enable innovation will demand substantial changes to IT infrastructure,skills and sourcing strategies. CIOs should transform the IT infrastructure and the ITorganization to support key emerging IoT trends and technologies. Obtaining the necessaryskills and partnerships will be a key priority.

■ An end-to-end IoT business solution comprises multiple technical components, which is notpossible to source from a single vendor. CIOs shall adopt a whole-solution thinking, shifting thefocus on the “best” technology product or brand to a focus on the best experience from asolution including related outcomes.

■ New technologies and trends introduce new risks alongside with new opportunities. CIOsshould consider risks from several levels including business, technology, legal/social as well asmarket risks, so as to evaluate: Will the technology or trend increase or decrease risk? Andwhat action do you need to take and when? This kind of preparation will help you to identify aright balance between opportunities and risks, to ensure a smooth introduction of technologiesinto your organization to enable business innovation.

Business Impact: IoT is enabling digital business innovation in virtually every industry, derived fromfour aspects: value model innovation, operating model innovation, product/service innovation andrelationship innovation, both within and outside the organization boundary. Meanwhile, theemergence of new sensors, innovative IoT chip technologies, edge technologies and mesharchitectures enable a wide diversity and possibility of use cases that we cannot imagine today.



Maturity: Emerging

Sample Vendors: Alibaba Cloud; China Mobile; China Telecom; Huawei; Microsoft; New H3CGroup; Quectel Wireless Solutions; Tencent; Tuya; Xiaomi

Recommended Reading: “Top Strategic IoT Trends and Technologies Through 2023”

“2019 Strategic Roadmap for IoT Network Technology”

“Use the IoT Platform Solution Reference Model to Help Design Your End-to-End IoT BusinessSolutions”

“Deploying IoT Analytics, From Edge to Enterprise”


NB-IoT

Analysis By: Peter Liu

Definition: NarrowBand Internet of Things (NB-IoT) is a low-power wide-area (LPWA) radiotechnology standard developed by 3GPP that provides wide-area connectivity for massivemachine-type communication for IoT. The NB-IoT specification was frozen in 3GPP LTE Release 13in June 2016, and enhanced features were added in LTE Release 14, which was frozen in August2018. NB-IoT is also known as LTE Cat-NB1.

Position and Adoption Speed Justification: NB-IoT, along with LTE for machine-typecommunications (LTE-M) (also known as LTE Cat-M1 and EC-GSM-IoT), is 3GPP’s effort to addressthe emerging LPWA market for supporting use cases for IoT in a WAN setting. As with other LPWAtechnologies, its key focus areas include low data rate, low power consumption and low modulecost, as the target use case is for connected devices that typically have a long life, low data rateand low data volume, together with a sporadic transmission frequency.

For NB-IoT, the cost of the wireless module is expected to drop below $5. The key trade-offs tosupport this price point include a narrow bandwidth (180 kilohertz) and low data rates (200 Kbps inhalf-duplex mode). However, one of the advantages of NB-IoT is that it can use the existing LTEspectrum that mobile network operators (MNOs) have been allocated. Then, NB-IoT can be rolledout via a simple software upgrade on existing LTE infrastructure.

Based on a GSA report, as of the end of March 2019, 88 operators in 50 countries have deployed orlaunched NB-IoT networks, up from 59 operators in March 2018. Commercial deployments includethose of China Mobile, China Telecom, China Unicom, T-Mobile (the Netherlands), Telia (Norway),TELUS (Canada) and Vodafone (Spain). NB-IoT has the potential to become a mainstream LPWAtechnology, but first it has to see significant price erosion to achieve cost parity with alternativevariants (LoRa, Sigfox and Random Phase Multiple Access [RPMA]).

In terms of chipset, more than 10 chipset suppliers announced their commercial NB-IoT products.Most of them support 3GPP Release 13. Regarding shipments, Huawei (HiSilicon Technologies) iscurrently in the leading position, benefiting from the mass rollout in China.

China is currently the biggest market for NB-IoT. NB-IoT was designated as the country’s preferredLPWA technology, and it plays a key role in national IoT policy. Backed by strong governmentsupport, all three operators claim to have rolled out the technology to tens of thousands of basestations. Developments in China will benefit the entire NB-IoT ecosystem. China’s three operators(China Mobile, China Telecom and China Unicom) have built the world’s largest NB-IoT network sofar by upgrading more than 1 million base stations across China to support NB-IoT. The target is toupdate more than 3 million base stations by 2025. The massive rollout and operator subsidy allowthe price of NB-IoT modules to fall to 20 renminbi (about $3). By the end of 2018, China Telecomoperators had more than 3 million NB-IoT connections.

User Advice: CIO and IT leaders who are responsible for IoT initiatives should:


https://gsacom.com/press-release/nb-iot-ltem-mar2019/

■ Test NB-IoT for more-demanding industrial use cases, as NB-IoT supports authentication(which is supported by the SIM card on mobile devices in 3GPP) and high reliability, which alicensed spectrum operation supports better than unlicensed alternatives.

■ Formulate an IoT strategy on the basis that investing in NB-IoT will be a long-term play but alow-risk strategy.

■ Work closely with NB-IoT ecosystems and effectively reduce the NB-IoT device price to achievescale and business value.

Business Impact: Among LPWA alternatives, NB-IoT will address the needs of use cases withhigher requirements for reliable connectivity, higher SLAs and robust 3GPP security. It is expectedthat NB-IoT can be enabled in existing radio access network (RAN) infrastructure without the needfor additional hardware upgrades, although vendors’ implementations vary.

NB-IoT is technically complicated and is covered by numerous patents, contributing to the cost ofimplementation at the endpoints. The main difference between NB-IoT and other proprietary LPWAtechnologies (such as LoRa, Sigfox or Weightless) is that NB-IoT is backed by 3GPP standards. Assuch, it will be deployed in a majority of mobile CSPs and will benefit from the stronger and richer3GPP ecosystem.

Finally, as an extension of LTE, NB-IoT will be deployed within mature networks, with establishedoperational and customer experience management key performance indicators (KPIs) andpractices, along with mature systems, such as self-organizing network (SON) IT support systems.




Sample Vendors: China Mobile; China Telecom; China Unicom; Ericsson; Huawei; Intel; Nokia;Qualcomm; u-blox; ZTE

Recommended Reading: “Exploit LPWA Networks Now — 5G Won’t Change Them”

“Competitive Landscape: IoT Mobile Virtual Network Operators”

Hyperconvergence

Analysis By: Uko Tian

Definition: Hyperconvergence is scale-out software-integrated infrastructure seeking operationalsimplification. Hyperconvergence provides a building block approach to compute, network andstorage on standard hardware under unified management. Hyperconvergence vendors buildappliances using off-the-shelf infrastructure, engage with system vendors that package software asan appliance, or sell software for use in a reference architecture or certified server.Hyperconvergence may also be delivered as a service or in a public cloud.


Position and Adoption Speed Justification: The adoption of hyperconvergence solutions israpidly expanding in China. Time to deploy and management simplicity are main values recognizedby users. In addition to the typical workloads running on hyperconverged infrastructure (HCI), suchas VDI, server consolidation and remote office/branch office (ROBO), HCI has also been marketedby vendors as a valid option to build private cloud. Midsize business are major users.

From supply side, the market remains fragmented. Local vendors take a leading role. Some of themare established data center vendors such as Huawei, New H3C Group and Sangfor. Some arepurely new players focusing on this area, such as SmartX and Zettakit. Local vendors that used tofocus on cloud, such as QingCloud and EasyStack, also extended offerings to HCI. Due to marketdiversification, there remains growth opportunity for small vendors. Local players show moreflexibility in pricing and better scalability, which bring them competence in the market.

User Advice:

■ IT leaders should implement hyperconvergence when agility, modular growth and managementsimplicity are of greatest importance. The acquisition cost of hyperconvergence may be higherand resource utilization rate lower than for three-tier architectures, but management efficiency isoften superior.

■ Plan strategically, but invest tactically. IT leaders should evaluate carefully vendors’ viability,service capability and product reliability because the market is still evolving.

■ Keep in mind that current hyperconvergence offerings in the market may vary significantly interms of performance, availability, data services and hypervisor support. Identify your use casesand workload characteristics to validate your HCI vendors’ solutions.

■ Be aware of the infrastructure and process challenge that HCI deployment may bring. Forexample, network maybe an issue when dealing with demanding workloads; and HCI willrequire consolidation of compute and storage in operations, budgets and capacity planning.

Business Impact: The lower total cost of ownership, modularity and scalability offer resource-constrained organizations fast deployment, simplified management and opportunities forautomation. Hyperconvergence is of particular value to midsize enterprises that can standardize onhyperconvergence and the remote sites of large organizations that need cloud-like managementefficiency with on-premises edge infrastructure.




Sample Vendors: Dell EMC; EasyStack; Huawei; New H3C Group; Nutanix; QingCloud; Sangfor;SmartX; Winhong; Zettakit

Recommended Reading: “Magic Quadrant for Hyperconverged Infrastructure”


“Solution Comparison for Four Hyperconverged and Software-Defined Infrastructure Solutions”

“Toolkit: Sample RFP for Hyperconverged Infrastructure”

“Use Hyperconverged Infrastructure to Free Staff for Public Cloud Management”

Private Cloud

Analysis By: Kevin Ji; Evan Zeng

Definition: Private cloud computing is a form of cloud computing used by only one organization, orone that ensures that an organization is completely isolated from others. As a form of cloudcomputing, it has full self-service, and full automation behind self-service and usage metering. Itdoes not have to be on-premises, or owned or managed by the enterprise.

Position and Adoption Speed Justification: In the Chinese market, enterprises use private cloudto enable enterprise agility and cost optimization, despite the scaling issues this approach presents.Based on private cloud dynamics in the Chinese market, Gartner has identified four differentapproaches, with available suppliers:

■ Commercial software. Leverage current virtualization software to build the private cloud. Thepurpose is infrastructure agility through virtualization and automation driven by faster virtualmachine provisioning. In this approach, VMware and Red Hat are reference vendors.

■ Hardware. Massive investment made in private cloud infrastructure, including hardware andsoftware. I&O leaders use this solution to optimize the IT operations process to deliver a full-function private cloud platform. In this approach, Huawei, New H3C Group, Inspur and Lenovoare reference vendors.

■ Solution leveraging public cloud. Public cloud providers leverage their cloud best practices todeliver an on-premises solution. In this approach, public cloud suppliers such as Alibaba Cloud,and Tencent Cloud are reference vendors.

■ Software delivery solution. Pure software solution providers normally use an open-stackframework to build a platform-neutral private cloud. I&O leaders use this solution to protectcurrent infrastructure investments and save on private cloud deployment costs. In thisapproach, EasyStack, 99Cloud and Huayun are reference vendors.

In China, the major investment in private cloud initiatives is the capital expenditure. Becausesoftware deployment is only a small part of the total private cloud expenditure, enterprise IT leadersprefer to leverage large server providers like Huawei or New H3C Group to help deploy the privatecloud, in combination with the hardware construction or refresh. This practice is popular ingovernment agencies and the finance industry.

Actually, this approach offers the advantage of the evaluation process being clear, with an all-in-onesolution. But it also brings on several challenges for enterprises, including:

■ The hardware price has a greater influence on decisions.


■ The solution vendor is locked in.

■ Ongoing professional services are needed to cover software maintenance.

Based on Gartner’s survey, IT operational readiness to deliver suitable value from private cloud ismuch harder than a technology implementation. We suggest concentrating on addressing thebusiness results of private cloud rather than hardware implementation. Here are somerecommendations:

■ Start small and think big — Clients need to choose a suitable workload for private cloud andmigrate it step by step, as it is very important to get the business’s support throughdemonstrating real value.

■ Set the process and structure first — Building a suitable process and structure for theorganization is a critical success factor for private cloud.

■ Define the success criteria — Embrace business values through operational excellencecriteria such as improving efficiency.

User Advice: Align private cloud initiatives with business outcomes by identifying the desiredbusiness result first and then defining the private cloud approach to support it. Determine whethervendor offerings suit your organization’s needs by evaluating successful use cases for the businessresult.

Control private cloud spending by taking a “just enough” approach to achieve the desired businessresult and leveraging public cloud for short-term, high scalable workloads. Launching anunnecessarily elaborate private cloud initiative will introduce technical complexity and excessivecosts, and may not align well with business goals.

Train attention on business results by building a private cloud governance team to focus ontechnology, process and talent to align each area with business goals.

Simplify private cloud deployments by prioritizing standardization early in the project.

Improve stability by shifting solution design from infrastructure resilience to application resilience, incollaboration with application leaders.

Business Impact: The primary benefit of private cloud is enterprise agility. However, building a full-function infrastructure as a service (IaaS) private cloud is complex and expensive, and many Gartnerclients struggle to achieve a good return on investment with private cloud projects. IT leaders needto leverage a suitable supplier to focus more on virtualization and automation in their private cloudinitiatives to maximize agility, as this will have the greatest impact on business value.





Sample Vendors: 99Cloud; Alibaba Cloud; EasyStack; FiberHome TelecommunicationTechnologies; Huawei; Microsoft; New H3C Group; Red Hat; UMCloud; VMware

Recommended Reading: “How to Overcome the Challenges of Adopting Private Cloud in China”

“Building ‘Just Enough’ Private Cloud With Virtualization Automation”

Digital Commerce Platform

Analysis By: Sandy Shen

Definition: Digital commerce platforms enable customers to purchase goods and services throughan interactive and self-service experience. The platform provides necessary information forcustomers to make purchase decisions and uses rules and data to present fully priced orders forpayment. It includes multiple channels such as web, mobile, stores, social, IoT devices and callcenters.

Position and Adoption Speed Justification: Chinese B2C organizations tend to rely more ononline marketplaces that account for more than 80% of the online retail transactions. This situationis unlikely to change in the next five years, a key reason why it will take five to ten years before thetechnology reaches the Plateau of Productivity. On the other hand, as consumers look fordifferentiated and personalized experiences, B2C organizations with strong brand recognition and alarge number of followers are deploying their own commerce platforms to own the customerexperience. They often start testing the water by using alternative channels such as WeChat beforethey build a full-blown commerce platform. Large B2B organizations are more likely than B2Corganizations to build their own platforms due to the lack of dominating marketplaces. They intentto improve customer experience, reduce sales costs, reach out to new customers or increase theirinfluence. There is an increasing use of AI to improve digital commerce performance inpersonalization, product recommendation, review management and supply chain management. Thiswill make building stand-alone commerce platforms more attractive than third-party marketplaces.

On the technology front, local commerce platform providers are not well established, and theirsolutions are not as comprehensive or sophisticated as their global peers, even though they are wellintegrated with the local ecosystem. Some providers choose to focus on marketplace integration tohelp organizations manage multiple channels, and some choose to become fully outsourced serviceproviders. Many platforms offer mobile and WeChat solutions that can be more attractive to SMBs,but may not be viable choices for larger enterprises. These factors mean it will take at least fiveyears before digital commerce platforms reach plateau.

User Advice: B2C organizations looking to deploy their own digital commerce platforms should beaware of the dominance of online marketplaces.

■ Consider starting with a marketplace store to better understand the local market before buildinga platform of your own. Follow the best practices suggested in “Balance Marketplaces andDirect Channels for Digital Commerce Growth and Customer Engagement” to take advantage ofboth channels.


■ Look for a marketplace integration solution that allow you to integrate with multiplemarketplaces and manage products and orders from a single place.

■ Follow the three-level cyclic process outlined in “How to Develop a Digital Commerce Strategy”to align digital commerce with your business goals and local market conditions.

■ Obtain management commitment for long-term investment as digital commerce especially yourown commerce platform doesn’t usually turn profit in a short time.

Organizations offering B2B services should start by clearly defining goals for digital commerce andtarget customer segments.

■ Define your B2B motivation and business model — whether it is to serve end customers orpartners, and what products and markets to be supported.

■ Investigate enterprise marketplace business model where you open the commerce platform tothird parties that include partners, suppliers, solution providers and other brands, and see howyou can benefit from this model strategically and/or financially.

■ Align digital commerce with your overall channel strategy to avoid conflict with your existingsales force and channel partners. Sometimes you want to engage partners while other timesyou may want to cut them out.

■ Deliver a consumer-like user experience and help customers streamline the purchase process.

Consider local providers, especially for integration with local channels and partners. Use Gartner’s“Toolkit: RFP for Digital Commerce Platforms” to compare vendor solutions on functionality,compatibility with your existing applications, integration needed and support level from the vendor.Be aware of local cybersecurity laws, the newly effective personal information standard and theupcoming cross-border data transfer guidelines that may impact your architectural and vendordecisions.

Business Impact: Organizations in China face a highly competitive market, not only because of thelarge number of brands and providers, but also the diverse customer preferences, businesspractices and infrastructure in regional markets. Although marketplaces are good at reaching a largenumber of customers and driving sales, companies don’t own customer experience and data.Customer data and insights are key in developing a differentiated experience and offering in thiscompetitive market, and this is a reason why more organizations are deploying their own platformsin addition to marketplace presence. B2B organizations have the chance to establish their ownplatforms due to the lack of dominant third-party marketplaces, and can even operate their ownmarketplace to create an ecosystem.





Sample Vendors: Baozun; HiShop; IBM WebSphere Commerce; Kingdee International SoftwareGroup; Magento; Oracle Commerce; Salesforce; SAP Hybris; ShopEx; Youzan

Recommended Reading: “How to Develop a Digital Commerce Strategy”

“Toolkit: RFP for Digital Commerce Platforms”

“Address Chinese Cybersecurity Law With This Playbook”

“China’s Data Privacy Standard Unfolds Measures for Its Cybersecurity Law”

Digital Financial Services

Analysis By: Sandy Shen

Definition: Digital financial services use digital technologies to deliver financial services to bothconsumers and businesses that include payment, investment, loan, credit, peer-to-peer (P2P)lending and banking.

Position and Adoption Speed Justification: After a large number of P2P platforms went bankruptin 2018, which led to hundreds of billions in losses, the Chinese government has put in place anumber of steps to register P2P platforms. It also asks for information to improve transparency ofthe platforms’ practices and safeguard the investors’ money. This has stabilized the market byresurrecting investor confidence. A number of leading platforms such as Lu and Yirendai havegained a strong foothold and positioned themselves for steady growth. At the same time, digitalgiants such as Alibaba and Tencent continue to offer a range of financial services to their users thatinclude digital banking, credit lending, loans, investment and insurance. These digital giantsespecially Alibaba benefited from the variety and large amount of data gathered, and the riskmanagement capabilities they have developed over the years are highly competitive compared totraditional financial institutions. They have also encouraged other digital players such as JD andXiaomi to launch their financial services portfolios.

Regulators encourage the competition from technology players as a means to stimulate innovation,and have introduced enabling policies to level the playing field and ensure fair competition. Theservice will evolve quickly in the next two to five years as internet firms and financial institutionscompete for customer wallet share and improve market positions.

User Advice: Financial institutions should:

■ Focus on presenting a strong value proposition to customers by leveraging their broad productofferings in financial services to solve customer problems.

■ Shift away from product development practices that primarily focus on the organization’s ownbenefits to those that help address customers’ real problems and deliver win-win results.

■ Balance the need to offer an easy-to-use UX versus the need to ensure security and managerisks.


■ Pay attention to the specification for personal information protection that requires proportionatecustomer data collection and a transparent consent mechanism.

■ Consider establish a subsidiary focusing on digital financial services that can experiment withinnovative technologies such as AI or machine learning for critical capabilities such as frauddetection and risk management. The subsidiary should also hire talent with internet backgroundand install metrics that encourage fast product development and trial-and-error approaches toquickly respond to customer needs and market trends.

Organizations with a large amount of customer data, especially transactional and behavioral data,that are looking for new sources of revenue can consider collaborating with digital financial serviceproviders to develop new offerings. Protect customer data if you need to share data with the partneras you don’t want to destroy customer trust when developing new products.

Technology providers with an ambition of launching their own digital financial services shouldidentify a target segment to avoid head-to-head competition with Alibaba or Tencent. Focus onniche segments such as use-case-specific consumer credit such as plastic surgery, internationaltravel and education — opportunities that are not yet fully exploited. Work with financial institutionsnot only for compliance but also for risk management expertise, which is key in making such anoffering successful.

Business Impact: Digital financial services is a digital transformation opportunity for financialinstitutions to change the way they design products and services, and the way they managecustomer relations. They need to break down the organizational silos to share information about thebusiness and customers, and to shorten the decision-making process in order to move quickly andcompete against the challengers. The biggest challenge is the change of organizational culture,more so than people, skills, funding or technologies. If they cannot transform into a more agile andinnovative culture, it will be difficult for financial institutions to compete with internet players, andthey will always play catch up.




Sample Vendors: Alibaba Group; Baidu; CreditEase; JD.com; Renrendai.com; Tencent; TongdunTechnology; Xiaomi; Yirendai

Recommended Reading: “The Art of Culture Hacking”

Public Cloud IaaS

Analysis By: Kevin Ji; Evan Zeng

Definition: Public cloud IaaS is a type of computing service in which scalable and elastic IT-enabledcapabilities are delivered as a service using internet technologies. Cloud infrastructure as a service


(IaaS) is a type of cloud computing service; it parallels the infrastructure and data center initiativesof IT.

Position and Adoption Speed Justification: Public cloud IaaS is well-accepted by Chinese smalland midsize clients in specific vertical industries such as retail, P2P, banking, media and gaming.The innovation demanded through digital business transformation is a key factor to trigger thischange. It requires application agility and faster time to market through public cloud enablement.China’s local cloud providers such as Alibaba Cloud and Tencent Cloud also use their businessecosystems to attract customers to use more of their cloud recourses, in order to help clients drivemore competitive advantage in the Chinese market.

Compared with the mature global market, for China’s large enterprises and government agencies,public cloud IaaS adoption is three to five years behind. Compared with last year, the majorchallenges have not changed and include compliance fulfillment, especially in highly regulatedindustries and those with data security concerns. In addition, a talent pool shortage, complexsystem architecture and stability are challenges addressed by Gartner in many client interactionscurrently.

Multinational corporations (MNCs) normally prefer to use public cloud, and align with global cloudstrategy to migrate their applications to Amazon Web Services (AWS) and Microsoft Azure in theChina region to meet Chinese privacy regulations such as cybersecurity laws. For digital businessdemands, Gartner has observed MNCs leveraging local cloud partners (such as Alibaba Cloud orTencent Cloud) through their strong internet business ecosystems.

User Advice: China’s cloud IaaS market is still growing. Chinese large enterprises should:

■ Focus on not only regulation compliance, but also business values, especially to get trial-and-error or faster time-to-market business demands fulfilled.

■ Strengthen their security capability on cloud management. The skill set of public cloud securitymanagement is different from that of on-premises data centers. Clients need to build a securityevaluation plan for cloud suppliers that is aligned with the security and audit teams, and set uprelated processes to monitor the results.

■ Assign the cloud architecture and cloud center of excellence teams to build a solid cloudstrategy, including public and private cloud migration plans.

■ To build the related talent, Gartner suggests that clients try using cloud on digital experienceapplications first.

For MNCs that plan to use public cloud in China:

■ Identify the business value of cloud migration, including compliance for cybersecurity, agility forlocal customer service and digital opportunities for new revenue.

■ Leverage global cloud suppliers to migrate applications to China, in order to fulfill the Chinesecompliance requirements with limited code revisions.

■ Evaluate the code migration cost to justify the use of a local public cloud supplier or globalbrands.


■ Use Alibaba Cloud or Tencent Cloud to address digital business opportunities in China.

■ Leverage MSP partners, since the cloud expert talent pool is very limited in China. Clients needto consider local partners to support cloud migration and hosting.

Business Impact: Based on technology trends, cloud IaaS will be broadly advantageous toinfrastructure modernization and will affect all sizes of Chinese businesses. Many enterprises wantto use public cloud to address new business-oriented architecture (middle platform in the Chinesemarket). However, Gartner has also observed the challenges that enterprises face in adopting it.

For large enterprises, many clients have tried to address digital business opportunities throughpublic cloud, especially digital business demands or faster time-to-market demands. However, mostof them are conservative in their migration of core application systems to public cloud.




Sample Vendors: Alibaba Group; Amazon Web Services; China Telecom; Huawei; Microsoft;QingCloud; Tencent

Recommended Reading: “China Summary Translation: ‘A Public Cloud Risk Model: AcceptingCloud Risk Is OK, Ignoring Cloud Risk Is Tragic’”

“China Summary Translation: ‘Cloud Computing Primer for 2019’”

“SWOT: Alibaba Cloud Services, Worldwide”

“Three Critical Factors Can Help You Decide Whether Alibaba Cloud Infrastructure as a Service IsRight for You”


Appendixes

Figure 3. Hype Cycle for ICT in China, 2018


Hype Cycle Phases, Benefit Ratings and Maturity Levels

Table 1. Hype Cycle Phases

Phase Definition

Innovation Trigger A breakthrough, public demonstration, product launch or other event generates significantpress and industry interest.

Peak of InflatedExpectations

During this phase of overenthusiasm and unrealistic projections, a flurry of well-publicizedactivity by technology leaders results in some successes, but more failures, as thetechnology is pushed to its limits. The only enterprises making money are conferenceorganizers and magazine publishers.

Trough ofDisillusionment

Because the technology does not live up to its overinflated expectations, it rapidly becomesunfashionable. Media interest wanes, except for a few cautionary tales.

Slope ofEnlightenment

Focused experimentation and solid hard work by an increasingly diverse range oforganizations lead to a true understanding of the technology’s applicability, risks andbenefits. Commercial off-the-shelf methodologies and tools ease the development process.

Plateau of Productivity The real-world benefits of the technology are demonstrated and accepted. Tools andmethodologies are increasingly stable as they enter their second and third generations.Growing numbers of organizations feel comfortable with the reduced level of risk; the rapidgrowth phase of adoption begins. Approximately 20% of the technology’s target audiencehas adopted or is adopting the technology as it enters this phase.

Years to MainstreamAdoption

The time required for the technology to reach the Plateau of Productivity.

Source: Gartner (July 2019)

Table 2. Benefit Ratings

Benefit Rating Definition

Transformational Enables new ways of doing business across industries that will result in major shifts in industrydynamics

High Enables new ways of performing horizontal or vertical processes that will result in significantlyincreased revenue or cost savings for an enterprise

Moderate Provides incremental improvements to established processes that will result in increased revenueor cost savings for an enterprise

Low Slightly improves processes (for example, improved user experience) that will be difficult totranslate into increased revenue or cost savings



Table 3. Maturity Levels

Maturity Level Status Products/Vendors

Embryonic ■ In labs ■ None

Emerging ■ Commercialization by vendors

■ Pilots and deployments by industry leaders

■ First generation

■ High price

■ Much customization

Adolescent ■ Maturing technology capabilities and processunderstanding

■ Uptake beyond early adopters

■ Second generation

■ Less customization

Early mainstream ■ Proven technology

■ Vendors, technology and adoption rapidly evolving

■ Third generation

■ More out-of-box methodologies

Maturemainstream

■ Robust technology

■ Not much evolution in vendors or technology

■ Several dominant vendors

Legacy ■ Not appropriate for new developments

■ Cost of migration constrains replacement

■ Maintenance revenue focus

Obsolete ■ Rarely used ■ Used/resale market only


Gartner Recommended ReadingSome documents may not be available as part of your current Gartner subscription.

Understanding Gartner’s Hype Cycles

2019 CIO Agenda: A China Perspective

Cool Vendors in Digital Business Innovation in China

Innovation Insight for 5G Networking — Cutting Through the Hype

How to Select the Right Local Partners for a Successful China Strategy

Market Guide for Cloud Infrastructure as a Service, China

Address Chinese Cybersecurity Law With This Playbook

How to Overcome the Challenges of Adopting Private Cloud in China


SWOT: Alibaba Cloud Services, Worldwide


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China Summary Translation: 'Hype Cycle for ICT in China, 2019